D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

Alternating active-phase algorithm for multimaterial topology optimization problems: a 115-line MATLAB implementation

Structural and Multidisciplinary Optimization - A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on...     

An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis

The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi-attribute decision making (MADM) method that is used to identify the most attractive alternative solution among a finite set of alternatives based on the simultaneous minimization of the distance from an ideal solution (IS) and the maximization of the distance from the nadir solution (NS). We propose an alternative compromise ratio method (CRM) using an efficient and powerful distance measure for solving the group MADM problems. In the proposed CRM, similar to TOPSIS, the chosen alternative should be simultaneously as close as possible to the IS and as far away as possible from the NS. The conventional MADM problems require well-defined and precise data; however, the values associated with the parameters in the real-world are often imprecise, vague, uncertain or incomplete. Fuzzy sets provide a powerful tool for dealing with the ambiguous data. We capture the decision makers’ (DMs’) judgments with linguistic variables and represent their importance weights with fuzzy sets. The fuzzy group MADM (FGMADM) method proposed in this study improves the usability of the CRM. We integrate the FGMADM method into a strengths, weaknesses, opportunities and threats (SWOT) analysis framework to show the applicability of the proposed method in a solar panel manufacturing firm in Canada.     

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Modelling,validating, and ranking of secure service compositions

In the world of large‐scale applications, software as a service (SaaS) in general and use of microservices, in particular, is bringing service‐oriented architectures to a new level: Systems in general and systems that interact with human users (eg, sociotechnical systems) in particular are built by composing microservices that are developed independently and operated by different parties. At the same time, SaaS applications are used more and more widely by enterprises as well as public services for providing critical services, including those processing security or privacy of relevant data. Therefore, providing secure and reliable service compositions is increasingly needed to ensure the success of SaaS solutions. Building such service compositions securely is still an unsolved problem. In this paper, we present a framework for modelling, validating, and ranking secure service compositions that integrate both automated services as well as services that interact with humans. As a unique feature, our approach for ranking services integrates validated properties (eg, based on the result of formally analysing the source code of a service implementation) as well as contractual properties that are part of the service level agreement and, thus, not necessarily ensured on a technical level.     

A new algorithm for modeling online search behavior and studying ranking reliability variations

We design an information retrieval algorithm that mimics the stochastic behavior of decision-makers (DMs) when evaluating the alternatives displayed by an online search engine. The algorithm consists of a decision tree that incorporates all the 1024 decision nodes that may arise from the information retrieval process of DMs. We calibrate the behavior of the algorithm to the one observed from online users and run several sets of 1,000,000 queries. Each query lets DMs decide which subset of the ten alternatives composing the initial page of results to click, allowing us to evaluate their behavior as ranking reliability is assumed to decrease when DMs decide not to click on an alternative. We compare the click-through rates (CTRs) obtained when modifying the degree of ranking reliability derived from the alternatives displayed on the first page of search results. We illustrate how the stability of the CTR prevails among the top-ranked alternatives within relatively reliable scenarios while it drops when imposing large initial decrements in reliability. The resulting consequences regarding the importance of relative ranking positions are analyzed, the top three alternatives exhibiting a generally contained decrease in their CTRs that contrasts with the cumulative pattern arising from the fourth position onwards.

     

Analysis of Ferroresonance Modes in Power Transformers Using Preisach-Type Hysteretic Magnetizing Inductance

The magnetic hysteresis phenomenon plays an important role in the ferroresonance behavior of a transformer. However, most existing transformer models for the analysis of the ferroresonance phenomenon either ignore the hysteresis effects or represent it based on the hysteresis major loop and scale it for lower current levels. Such a modeling approach does not reflect the actual physical behavior of the magnetic core with respect to the ferroresonance phenomenon. This paper, based on Preisach theory, introduces an accurate model of hysteretic inductor to represent a single-phase transformer for the investigation of the ferroresonance phenomenon. Based on the developed model, time-domain simulation studies show that even in the case of a single-phase transformer where no magnetic coupling exists between the three-phases, all known types of ferroresonance, including fundamental, sub-harmonic, quasi-periodic and chaotic modes, may occur. The effects of hysteresis and its initial conditions, grading capacitors and transformer winding capacitance on occurrence of ferroresonance and its change of mode are also discussed     

Adaptive noise reduction using numerically stable fast recursive least squares algorithm

This paper is concerned with adaptive noise reduction based on the fast recursive least squares (FRLS) algorithm. It is well known that the fast recursive least squares (FRLS) algorithm suffers from numerical instability when operating under the effects of finite precision arithmetic. Several numerical solutions of stabilization were proposed in the case of stationary signals. In this work a new version of a numerically stable FRLS algorithm (NS‐FRLS) is proposed. The stability characteristics of this new stabilized algorithm are analysed. The analysis is based on a linear model for the errors in the states of the adaptive filter. Experimental results confirm the merits of adaptive filtering with the NS‐FRLS algorithm over optimum filtering using the solution provided by Wiener–Hopf equations. Copyright © 2006 John Wiley & Sons, Ltd.     

Coupling ultraviolet photolysis and biofiltration for enhanced degradation of aromatic air pollutants

Coupling UV photolysis and biofiltration was evaluated as an effective treatment strategy for the enhanced degradation of hardly biodegradable aromatic volatile organic compounds (VOCs). o‐Xylene, a recalcitrant and poorly water‐soluble VOC, was used as a model compound and treated in two parallel treatment systems with and without UV pretreatment. Contaminated streams with flow rates of 0.186–0.384 m³ h^?1 and inlet o‐xylene concentrations of up to 0.22 g m^?3 were passed through the treatment system. About 20% (between 10 and 35%) of o‐xylene was converted into water‐soluble intermediates during the UV photolysis stage, which partially oxidized o‐xylene to more water‐soluble and biodegradable byproducts. The untreated contaminant along with the byproducts of UV photolysis was then removed effectively in the biofiltration stage, with improvements of up to 100% compared with the control biofiltration process. The results suggested that combined UV photolysis–biofiltration is promising as an effective technique to eliminate hydrophobic and recalcitrant organic compounds from contaminated air steams. Copyright © 2005 Society of Chemical Industry